EMID-Emotion-Matching

	---
	dataset_name: EMID-Emotion-Matching
	annotations_creators:
	- expert-generated
	language:
	- en
	license: cc-by-nc-sa-4.0
	pretty_name: EMID Music ↔ Image Emotion Matching Pairs
	tags:
	- audio
	- music
	- image
	- multimodal
	- emotion
	- contrastive-learning
	task_categories:
	- audio-classification
	- image-classification
	- visual-question-answering
	---

	# EMID-Emotion-Matching

	`orrzohar/EMID-Emotion-Matching` is a derived dataset built on top of
	the Emotionally paired Music and Image Dataset (EMID) from ECNU (`ecnu-aigc/EMID`).
	It is designed for music ↔ image emotion matching with Qwen-Omni–style models.

	Each example contains:

	- `audio`: mono waveform stored as `datasets.Audio` (HF Hub preview can play it)
	- `sampling_rate`: sampling rate used when decoding (typically 16 kHz)
	- `image`: a single image (`datasets.Image`)
	- `same`: `bool`, whether the audio and image are labeled with the same emotion
	- `emotion`: normalized image emotion tag (e.g. `amusement`, `excitement`) for positive pairs; empty string for negatives
	- `question`: natural-language question used to prompt the model (several templates are mixed)
	- `answer`: canonical supervision text (`yes - {emotion}` for positives, `no` for negatives)

	\| column \| type \| description \|
	\| -------------- \| ------------------------------- \| ----------- \|
	\| `audio` \| `datasets.Audio (16k mono)` \| decoded waveform; HF UI can play it \|
	\| `sampling_rate`\| `int32` \| explicit sample rate mirrored beside the `Audio` column \|
	\| `image` \| `datasets.Image` \| PIL.Image-compatible object \|
	\| `same` \| `bool` \| `True` if the pair is emotion-aligned \|
	\| `emotion` \| `string` \| normalized emotion label for positives, `""` otherwise \|
	\| `question` \| `string` \| user prompt template \|
	\| `answer` \| `string` \| canonical supervision text (`yes - {emotion}` / `no`) \|

	The original EMID row has one music clip and up to three tagged images
	(`Image1`, `Image2`, `Image3`). For each `(audio, image)` pair we create:

	- 1 positive example: the audio and its own tagged image (`same = True`, `emotion = image_tag`)
	- NEGATIVES_PER_POSITIVE = 1 negative example: the same audio paired with an image drawn
	from a different emotion tag (`same = False`, `emotion = ""`)

	With `MAX_SOURCE_ROWS = 4000`, this yields ~24,000 examples (positives + negatives),
	which we then split into:

	- `train`: 19,200 examples
	- `test`: 4,800 examples

	## Source Data (EMID)

	The base EMID dataset is described in:

	- Emotionally paired Music and Image Dataset (EMID)
	Y. Guo, J. Li, et al.
	arXiv:2308.07622 — "Emotionally paired Music and Image Dataset (EMID)"
	<https://arxiv.org/abs/2308.07622>

	EMID contains 10,738 unique music clips, each paired with three images in the same
	emotional category, plus rich annotations:

	- `Audio_Filename`: unique filename of the music clip
	- `genre`: letter A–M, one of 13 emotional categories
	- `feeling`: distribution of free-form feelings reported by listeners (% per feeling)
	- `emotion`: ratings on 11 emotional dimensions (1–9)
	- `Image{1,2,3}_filename`: matched image filenames
	- `Image{1,2,3}_tag`: image emotion category (e.g. `amusement`, `excitement`)
	- `Image{1,2,3}_text`: GIT-generated captions
	- `is_original_clip`: whether this is an original or expanded clip

	For more details, see the EMID README and the paper above.

	## How This Derived Dataset Was Built

	The script `prepare_emid_pairs.py` performs the following steps offline:

	1. Load `ecnu-aigc/EMID` (train split) and decode:
	- `Audio_Filename` with `Audio(decode=True)`
	- `Image{1,2,3}_filename` with `datasets.Image(decode=True)`
	2. Optionally cap the number of source rows with `MAX_SOURCE_ROWS` (default 4000).
	3. Build an image pool keyed by normalized emotion tags.
	4. For each EMID row and each available image (up to 3 per row):
	- Create a positive pair `(audio, image, same=True, emotion=image_tag)`.
	- Sample `NEGATIVES_PER_POSITIVE` images from different emotion tags to form negatives.
	5. Normalize the emotion strings (lowercase, replace spaces and punctuation with `_`).
	6. Draw a random question from a small set of Qwen-style templates and attach it as `question`.
	7. Store the mono waveform as `datasets.Audio` and the image as `datasets.Image` so
	that downstream scripts can call `datasets.load_dataset` without extra decoding logic.
	8. Split into train/test with `TRAIN_FRACTION = 0.8`.

	This yields a simple, flat structure that is convenient for SFT / contrastive training
	with Qwen2.5-Omni (or other multimodal LMs), without re-doing negative sampling or
	audio/image decoding inside notebooks.

	## Suggested Usage

	```python
	from datasets import load_dataset

	ds = load_dataset("orrzohar/EMID-Emotion-Matching")
	train_ds = ds["train"]
	test_ds = ds["test"]

	ex = train_ds[0]
	audio = ex["audio"] # dict with "array" + "sampling_rate"
	sr = ex["sampling_rate"] # int
	image = ex["image"] # PIL.Image.Image
	same = ex["same"] # bool
	emotion = ex["emotion"] # str
	question = ex["question"] # str
	answer = ex["answer"] # str
	```

	In the Qwen-Omni demos, we typically:

	- Use `question` as the user prompt,
	- Provide `audio` and `image` as multimodal inputs, and
	- Supervise the model with the provided `answer` (or regenerate your own phrasing from `same`/`emotion`).

	## License

	This derived dataset inherits the license from EMID:

	- CC BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike 4.0 International)

	You must:

	- Use the data only for non-commercial purposes.
	- Provide appropriate attribution to the EMID authors and this derived dataset.
	- Distribute derivative works under the same license.

	Please refer to the full license text for details:
	<https://creativecommons.org/licenses/by-nc-sa/4.0/>

	If you use this dataset in academic work, please cite the EMID paper and, if appropriate,
	this derived dataset as well.